All the previously mentioned works on relation extraction assume that entities are already identified by a named entity recognition system. They classify the relation between two given entities at the sentence level or the bag-of-sentences level. These models depend on an external named entity recognition system to identify the entities in a text. Recently, some researchers [1]}, [2]}, [3]}, [4]} tried to remove this dependency. They tried to bring the entity recognition and relation identification tasks closer by sharing their parameters and optimizing them together. They first identify all the entities in a sentence and then find the relation among all the pairs of identified entities. Although they identify the entities and relations in the same network, they still identify the entities first and then determine the relation among all possible pairs in the same network. So these models miss the interaction among the relation tuples present in a sentence. These approaches resemble the pipeline approach to some extent.
